1. Technical Field of the Invention
The invention relates generally to communication systems; and, more particularly, it relates to a system and method that are operable to perform modification of branch metrics/decision metrics for processing bit-soft decisions to account for phase noise impact on cluster variance.
2. Description of Related Art
Communication systems transmit digital data through imperfect communication channels. These symbols may undergo some undesirable corruption due to the imperfection of the communication channel. One effort to try to avoid such situations is focused on performing forward error correction (FEC) coding. However, there is typically some difficulty in extracting the information contained within these symbols after they have been undesirably altered within the communication channel. There exist some methods that seek to curb the effect that the communication channel has had on the data; one such method includes employing using Decision Feedback Equalizers (DFEs). However, even after the incoming signal has been equalized, the extraction of the data, that has undergone some alteration due to the channel effects, is still a probabilistic determination.
Many communication systems, particularly in a receiver, need to perform the analog to digital transformation of an incoming signal. In doing so, there is oftentimes an uncertainty in whether a sample of the incoming analog signal is properly transformed into a 1 or a 0 in the digital realm; for example, there is not a 100% certainty that an incoming signal is actually a 1 or actually a 0—there is some probability associated with the decision. In higher-level encoding/decoding systems, e.g., QPSK/4 QAM, 16 QAM, 64 QAM, 256 QAM, and 1024 QAM etc., there are several bits per symbol that need to be transformed to either a 1 or a 0. In 16 QAM applications, a receiver extracts 4 bits of data from each symbol. In the QAM modulation scheme, each symbol includes an in-phase component and a quadrature component. For the 16 QAM modulation type, the decision block maps the in-phase and quadrature components contained in the symbol to a 16 QAM constellation and decides the values of the four bits that are carried by the symbol. Within these various systems, the constellation points that are used to modulate/demodulate the data do not all have identical cluster variances (CVs).
The decisions made in extracting bits from a particular symbol are referred to as “bit-soft decisions.” Bit-soft decisions not only map the symbol into bits but also produce a decision metric/branch metric related to the probability that the decision was correct based upon how well the received, in-phase, and quadrature voltages of a symbol correspond to the symbol in the constellation. The terminology of “branch metric” is often used interchangeably with “decision metric,” and this convention will be followed in this document. The decoder operates based on the premise that there are only a finite number of possible states of the encoder and that given a certain number of consecutive states, the input bit may be predicted that would have caused a particular state transition. The decoder generates a “branch metric” (or “decision metric”) for each of the possible “state transitions” from one state to another. The coding method maintains a “decision metric” associated with every state that is the sum of the metric at its predecessor state and the metric associated with the branch that caused the state transition. This metric may be termed the cumulative metric or accumulated metric, and the decoder generates the cumulative metric for all of the states. The different states and the transition from one state to another can be represented in a diagram, namely, a trellis diagram. For various possible allowable state transition sequences through the trellis (the allowable paths through the trellis), the decision metric associated with the sequence of branches of the trellis diagram are summed together, and the smallest sum is selected as the actual state transition and enables the identification of the best estimate of the decoded data. It is also noted, without loss of generality, the sign of the metrics may also be changed so that the largest sum would be the best estimate.
As mentioned above, the constellation points that are used to modulate/demodulate the data do not all have identical cluster variances (CVs). Conventional and prior art commonly calculate the cluster variances of points within the constellations used to demodulate received data. Oftentimes, the cluster variance of the constellation points closer to the origin of the constellation is tighter than those further out from the constellation's origin for the same signal to noise ratio (SNR), particularly for higher SNR where phase noise is significant, and the larger energy constellation points are further from the constellation's origin.
Some communication systems are particularly susceptible to phase noise in the signal. When a communication receiver demodulates the data, the phase noise can be problematic, in that, the various points in the constellation may have significantly different cluster variances. In some communication systems, such as multi-tone modulation formats, the degradation of a transmitted signal, due to phase noise, is increased by the interaction of the various tones within the transmitted signal. Phase noise may also be introduced by inadequacies in the sampling portions of the receiver, including phase noise inherent in the generation of the LO (local oscillator) used for down-converting from the transmission frequency. (Similarly, phase noise is introduced at the transmitter as well.) In higher order modulation types such as 16 QAM, 64 QAM, etc., this phase noise has a large impact on the mapping of symbols into the constellations. Phase noise will often cause outer-constellation points to have a greater cluster variance than inner-constellation points for an equal phase noise contribution, again, in a situation where the SNR is relatively high. To illustrate this situation in one embodiment: for a 16 QAM implementation, outer constellation points will typically suffer nine times as large of a cluster variance (three times standard deviation) for a same amount of phase noise as compared to the inner constellation points. The bit soft decisions that are generated and provided to a decoder are ultimately significantly affected by any introduction of this phase noise.
Further limitations and disadvantages of conventional and traditional systems will become apparent to one of skill in the art through comparison of such systems with the invention as set forth in the remainder of the present application with reference to the drawings.